State aggregation for dynamic programming has been studied for long among the communities of control theory and operations research [1]}, [2]}, [3]}, [4]}. Successive approximation is one of the main tools leveraged to form a trade-off between the accuracy of the solution of the dynamic programming problem and its computational complexity [5]}, [1]}. Later, adaptive algorithms for state aggregation were proposed which could recompose the aggregation of states during the iterations [4]}. The major driving force for researchers, in the past, to work on this problem has been to enhance the computational efficiency of the algorithms. However, the algorithms that they have made available, can now be used to efficiently represent the state of environment while minimizing the degradation of the objective function. This area is also well explored by the community of computer science and is addressed in the next subsection.
